Harnessing the Social Annotations for Tag Refinement in Cultural Multimedia

Authors

  • Kirubai Dhanaraj  Department of Computer Science, Bishop Heber College, Tiruchirappalli, India
  • Rajkumar Kannan  Department of Computer Science, Bishop Heber College, Tiruchirappalli, India

Keywords:

Tag Refinement, Tag Localization, Temporal Consistency, Social Annotations, Multimedia Retrieval, SURF feature

Abstract

Videos are the source of social multimedia for the past few years and going to be the major source of all communications in the near future. On the other hand multimedia retrieval techniques lack in semantic context annotations for the video. Though the social media has numerous annotated tags and comments for similar image contents, it is not properly correlated with the context of the video retrieval techniques. In this paper we propose a method for video tag refinement and temporal localization for cultural multimedia. In this method the social annotations are exhibited to harness the temporal consistency of the video.

References

  1. L. S. Kennedy, S.-F. Chang, I. V. Kozintsev, To search or to label? Predicting the performance of search-based automatic image classifiers, in:Proc. of ACM MIR, Santa Barbara, CA, USA, 2006, pp. 249-258.
  2.  B. Sigurbj¨ornsson, R. van Zwol, Flickr tag recommendation based on collective knowledge, in: Proc. Of WWW, Beijing, China, 2008, pp. 327-336.
  3. X. Li, T. Uricchio, L. Ballan, M. Bertini, C. G. M. Snoek, A. Del Bimbo, Socializing the semantic gap: A comparative survey on image tag assignment, refinement and retrieval, arXiv preprint arXiv:1503.08248 (2015).
  4. D. Liu, X.-S. Hua, L. Yang, M.Wang, H.-J. Zhang, Tag ranking, in: Proc.of WWW, Madrid, Spain, 2009, pp. 351-360.
  5. A. Makadia, V. Pavlovic, S. Kumar, A new baseline for image annotation, in: Proc. of ECCV, Marseille, France, 2008, pp. 316-329.
  6. L. Ballan, M. Bertini, T. Uricchio, A. Del Bimbo, Data-driven approaches for social image and video tagging, Multimedia Tools and Applications 74 (2015) 1443-1468.
  7. Y. Yang, Y. Yang, Z. Huang, H. T. Shen, Tag localization with spatial correlations and joint group sparsity, in: Proc. of CVPR, Providence, RI, USA, 2011, pp. 881-888.
  8. X. Cao, X.Wei, Y. Han, Y. Yang, N. Sebe, A. Hauptmann, Unified dictionary learning and region tagging with hierarchical sparse representation, Computer Vision and Image Understanding 117 (2013) 934-946. 
  9. J. Song, Y. Yang, Z. Huang, H. T. Shen, J. Luo, Effective multiple feature hashing for large-scale near-duplicate video retrieval, IEEE Transactions on Multimedia 15 (2013) 1997-2008.
  10. Z. Wang, M. Zhao, Y. Song, S. Kumar, B. Li, YouTubeCat: Learning to categorize wild web videos, in: Proc. of CVPR, San Francisco, CA, USA, 2010, pp. 879-886.
  11.  H. Li, L. Yi, Y. Guan, H. Zhang, DUT-WEBV: A benchmark dataset for performance evaluation of tag localization for web video, in: Proc. Of MMM, Huangshan, China, 2013, pp. 305-315.
  12. L. Ballan, M. Bertini, A. Del Bimbo, M. Meoni, G. Serra, Tag suggestion and localization in user-generated videos based on social knowledge, in: Proc. of ACM Multimedia, WSM Workshop, Firenze, Italy, 2010, pp. 1-5.
  13. H. Li, L. Yi, B. Liu, Y. Wang, Localizing relevant frames in web videos using topic model and relevance filtering, Machine Vision and Applications 25 (2014) 1661-1670.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2018-02-28

Issue

Volume 3, Issue 1, January-February-2018

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Kirubai Dhanaraj, Rajkumar Kannan, " Harnessing the Social Annotations for Tag Refinement in Cultural Multimedia, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 1, pp.1802-1808, January-February-2018.